Answering predetermined questions was based on a centralized and well-articulated data model, but failed to enable data exploration, experimentation and evolution of the data-driven business strategy. These failures only get worse with time as more data needs to be integrated and the platform becomes more fragile to change. Ultimately this fails to deliver timely and competitive business results. As we’ve seen, providing known data to answer known questions can be done using many well-known data architectures, but that’s only part of the story.
Adopting a data lakehouse architecture over a traditional data warehouse comes with many business benefits. In fact, these differences are often the key reason that organizations make the transition from older technologies to modern ones. Let’s explore the business case for the adoption of a data lakehouse, also known as modern data lake, and see how it can improve versatility, maintain performance, and reduce costs. To do this, we’ll compare the differences between a data warehouse and data lake then discuss the hybrid advantage of a data lakehouse.
Of all the seventy-plus speakers at the festival, there was one presentation that I found to be particularly interesting – and not because the speaker also happens to be our customer. That presentation was from Lutz Künneke, Director of Engineering, and Isa Inalcik, Senior Data Engineer, at BestSecret, a leading European online destination for off-price fashion based near Munich, Germany. As Künneke got to the stage, the first words out of his mouth were: “We are moving off of Snowflake.”
We explore how AI and data products are coming together to improve the user experience and to accelerate the delivery of innovative business solutions. Some of these are based on conceptual discussions, and others are based on actual use cases. Early strategic vision is usually painted with some crayons (curiosity and imagination) and watercolors (experience and detail).
Clinicians often make critical patient-care decisions based on anecdotal observations and personal experiences, emphasizing the urgent need for faster access to real-world evidence to support and enhance clinical decision-making.
Unlocking the Potential: Language Models (LLMs) offer exciting possibilities in data analytics and query processing. Join us as we explore their strengths, limitations, and our future plans at Starburst
In this article, we’ll discuss four traps that can disrupt data product development and how to avoid falling into them. These bad practices are focusing on quantity over quality, ignoring stakeholder feedback, overlooking collaboration, and putting off governance.
Despite innovations in data architecture, infrastructure, and analytics, most organizations today still struggle to realize the promised value of data. Learn how the data mesh principle of data as a product can help, as part of a data mesh initiative or as a stand-alone strategy.